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Found 9 papers, 2 papers with code
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
Recently, neural networks have proven to be effective in performing speech coding task at low bitrates.
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators
Packet loss is a common and unavoidable problem in voice over internet phone (VoIP) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
An Exploration of Task-decoupling on Two-stage Neural Post Filter for Real-time Personalized Acoustic Echo Cancellation
Utilization of speaker representation has extended the frontier of AEC, thus attracting many researchers' interest in personalized acoustic echo cancellation (PAEC).
Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model
With fewer feature dimensions, filter banks are often used in light-weight full-band speech enhancement models.
Two-stage Neural Network for ICASSP 2023 Speech Signal Improvement Challenge
Achieving 0. 446 in the final score and 0. 517 in the P. 835 score, our system ranks 4th in the non-real-time track.
Personalized speech enhancement combining band-split RNN and speaker attentive module
Target speaker information can be utilized in speech enhancement (SE) models to more effectively extract the desired speech.
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification
We propose a novel neural model compression strategy combining data augmentation, knowledge transfer, pruning, and quantization for device-robust acoustic scene classification (ASC).
A Two-Stage Approach to Device-Robust Acoustic Scene Classification
To improve device robustness, a highly desirable key feature of a competitive data-driven acoustic scene classification (ASC) system, a novel two-stage system based on fully convolutional neural networks (CNNs) is proposed.
Ranked #1 on
Acoustic Scene Classification
on TAU Urban Acoustic Scenes 2019
(using extra training data)
Device-Robust Acoustic Scene Classification Based on Two-Stage Categorization and Data Augmentation
On Task 1b development data set, we achieve an accuracy of 96. 7\% with a model size smaller than 500KB.
